@mastersthesis {1502496,
	title = {Not a Lone Ranger: Unleashing Defender Teamwork in Security Games },
	year = {2015},
	type = {PhD thesis},
	abstract = {Game theory has become an important research area in handling complex security resource allocation and patrolling problems. Stackelberg Security Games (SSGs) have been used in modeling these types of problems via a defender and an attacker(s). Despite recent successful real-world deployments of SSGs, scale-up to handle defender teamwork remains a fundamental challenge in this field. The latest techniques do not scale-up to domains where multiple defenders must coordinate time-dependent joint activities. To address this challenge, my thesis presents algorithms for solving defender teamwork in SSGs in two phases. As a first step, I focus on domains without execution uncertainty, in modeling and solving SSGs that incorporate teamwork among defender resources via three novel features: (i) a column-generation approach that uses an ordered network of nodes (determined by solving the traveling salesman problem) to generate individual defender strategies; (ii) exploitation of iterative reward shaping of multiple coordinating defender units to generate coordinated strategies; (iii) generation of tighter upper-bounds for pruning by solving security games that only abide by key scheduling constraints. In the second stage of my thesis, I address execution uncertainty among defender resources that arises from the real world by integrating the powerful teamwork mechanisms offered by decentralized Markov Decision Problems (Dec-MDPs) into security games. My thesis offers the following novel contributions: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that utilizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the number of targets and resources to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. Different mechanisms, from both solving situations with and without execution uncertainty, may be used depending on the features of the domain. This thesis opens the door to a powerful combination of previous work in multiagent systems on teamwork and security games.},
	author = {Shieh, Eric}
}
